Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation

Kaan Akan, Yucel Yemez

Keywords: object-centric learning, slot attention, unsupervised object discovery, object segmentation

TL;DR: A novel object-centric learning approach integrates slot attention with pretrained diffusion models, enhancing object discovery and image generation while reducing computational costs using adapters and attention mask guidance.

Abstract: We present SlotAdapt, an object-centric learning method that combines slot attention with pretrained diffusion models by introducing adapters for slot-based conditioning. Our method preserves the generative power of pretrained diffusion models, while avoiding their text-centric conditioning bias. We also incorporate an additional guidance loss into our architecture to align cross-attention from adapter layers with slot attention. This enhances the alignment of our model with the objects in the input image without using external supervision. Experimental results show that our method outperforms state-of-the-art techniques in object discovery and image generation tasks across multiple datasets, including those with real images. Furthermore, we demonstrate through experiments that our method performs remarkably well on complex real-world images for compositional generation, in contrast to other slot-based generative methods in the literature. The project page can be found at https://kaanakan.github.io/SlotAdapt/

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2687